pytorch 回归

import torch
from torch.autograd import Variable
import  torch.nn.functional as F
import matplotlib.pyplot as plt



x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim = 1)
y = x.pow(2) + 0.2*torch.rand(x.size())

x, y = Variable(x), Variable(y)

# plt.scatter(x.data.numpy(), y.data.numpy())
# plt.show()


class Net(torch.nn.Module):
    def __init__(self, n_feature, n_hidden, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_hidden)
        self.predict = torch.nn.Linear(n_hidden, n_output)
    def forward(self, x):
        x = F.relu(self.hidden(x))
        x = self.predict(x)
        return x
net = Net(n_feature=1,n_hidden=10,n_output=1)
print(net)


optimizer = torch.optim.SGD(net.parameters(), lr = 0.4)
loss_func = torch.nn.MSELoss()

plt.ion()

for t in range(1000):
    prediction = net(x)

    loss = loss_func(prediction, y)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if t % 5 == 0:
        plt.cla()
        plt.scatter(x.data.numpy(), y.data.numpy())
        plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw = 5)
        plt.text(0.5, 0, 'Loss=%.4f' % loss.item(), fontdict={'size':20, 'color':'red'})
        plt.pause(0.1)
plt.ioff()
plt.show()

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